# Hugging Face Space Configuration

This document contains the configuration needed to deploy this application as a Hugging Face Space.

## Space Configuration

### Basic Settings
- **Space Name**: `chat-with-github-repo`
- **Space Type**: `Gradio`
- **Python Version**: `3.11`
- **Visibility**: `Public`

### Environment Variables (Optional)

Set these in your Hugging Face Space settings for better performance:

```
HUGGINGFACE_API_KEY=your_hf_token_here
GITHUB_TOKEN=your_github_token_here
```

### Hardware Requirements

- **CPU**: Basic (free tier works)
- **RAM**: 8GB+ recommended for larger repositories
- **Storage**: 10GB+ for model caching

## Deployment Steps

1. **Create a new Hugging Face Space**:
   - Go to https://huggingface.co/new-space
   - Choose "Gradio" as the Space SDK
   - Set the space name and visibility

2. **Upload files**:
   - Upload all files from this directory to your space
   - Ensure the main `app.py` file is in the root directory

3. **Configure environment variables** (optional):
   - Go to your space settings
   - Add the environment variables listed above
   - This improves rate limits and enables private repo access

4. **Deploy**:
   - The space will automatically build and deploy
   - First deployment may take 5-10 minutes due to model downloads

## File Structure for Hugging Face Space

```
your-space/
├── app.py                 # Main Gradio application
├── requirements.txt       # Python dependencies
├── README.md             # Space documentation
├── config.py             # Configuration settings
├── services/             # Service modules
│   ├── __init__.py
│   ├── github_service.py
│   ├── embedding_service.py
│   └── chat_service.py
├── utils/                # Utility modules
│   ├── __init__.py
│   └── file_processor.py
└── models/               # Data models
    ├── __init__.py
    └── schemas.py
```

## Performance Optimization

### For Free Tier:
- Uses lightweight embedding model (`all-MiniLM-L6-v2`)
- Processes files in batches
- Implements file size limits
- Caches models locally

### For Better Performance:
- Upgrade to paid hardware
- Use larger embedding models
- Increase batch sizes
- Add Redis caching

## Troubleshooting

### Common Issues:

1. **Out of Memory**:
   - Reduce batch size in embedding service
   - Use smaller embedding model
   - Upgrade hardware

2. **Slow Processing**:
   - Add Hugging Face API token for better rate limits
   - Use GPU hardware
   - Optimize chunk sizes

3. **Git Clone Failures**:
   - Add GitHub token for private repos
   - Check repository URL format
   - Ensure repository is public

### Debug Mode:
Set `debug=True` in `demo.launch()` for detailed error messages.

## Monitoring

Monitor your space performance:
- Check space logs for errors
- Monitor memory usage
- Track processing times
- Review user feedback

## Updates

To update your space:
1. Modify files locally
2. Upload changed files to your space
3. Space will automatically rebuild
4. Test functionality after deployment